_base_ = [
    "../_base_/models/retinanet_r50_fpn.py",
    "../_base_/datasets/coco_detection.py",
    "../_base_/schedules/schedule_2x.py",
    "../_base_/default_runtime.py",
]
# model settings
norm_cfg = dict(type="GN", num_groups=32, requires_grad=True)
model = dict(
    pretrained="torchvision://resnet101",
    backbone=dict(depth=101),
    bbox_head=dict(
        _delete_=True,
        type="SABLRetinaHead",
        num_classes=80,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        approx_anchor_generator=dict(
            type="AnchorGenerator",
            octave_base_scale=4,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[8, 16, 32, 64, 128],
        ),
        square_anchor_generator=dict(
            type="AnchorGenerator",
            ratios=[1.0],
            scales=[4],
            strides=[8, 16, 32, 64, 128],
        ),
        norm_cfg=norm_cfg,
        bbox_coder=dict(type="BucketingBBoxCoder", num_buckets=14, scale_factor=3.0),
        loss_cls=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_bbox_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.5),
        loss_bbox_reg=dict(type="SmoothL1Loss", beta=1.0 / 9.0, loss_weight=1.5),
    ),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type="ApproxMaxIoUAssigner",
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0.0,
            ignore_iof_thr=-1,
        ),
        allowed_border=-1,
        pos_weight=-1,
        debug=False,
    ),
)
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="Resize",
        img_scale=[(1333, 480), (1333, 960)],
        multiscale_mode="range",
        keep_ratio=True,
    ),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
data = dict(train=dict(pipeline=train_pipeline))
# optimizer
optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)
